Machine learning, despite having more than 50 years of history in subsurface disciplines, has largely remained a niche workflow, frequently performed in isolation with lack of repeatability. While advances in computing and programming language have opened up access to machine learning as a tool, we have yet to see the same growth in operational efficiency experienced by other segments and verticals within the energy industry. Application of ML toward data conditioning and workflow set-up could save geoscientists hundreds of hours each year, allowing for faster delivery of results and improving standardisation and consistency across departments.
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